Overview

Brought to you by YData

Dataset statistics

Number of variables	26
Number of observations	231
Missing cells	874
Missing cells (%)	14.6%
Duplicate rows	0
Duplicate rows (%)	0.0%
Total size in memory	193.1 KiB
Average record size in memory	856.2 B

Variable types

Text	2
DateTime	1
Categorical	15
Numeric	8

Alerts

`Cluster_6` has constant value "1"	Constant
`Estado` is highly overall correlated with `cant_MontoLimite`	High correlation
`anio_preinscripcion` is highly overall correlated with `antiguedad` and 1 other fields	High correlation
`antiguedad` is highly overall correlated with `anio_preinscripcion` and 1 other fields	High correlation
`cant_Apoderado` is highly overall correlated with `cant_MontoLimite` and 2 other fields	High correlation
`cant_MontoLimite` is highly overall correlated with `Estado` and 9 other fields	High correlation
`cant_antecedentes` is highly overall correlated with `cant_MontoLimite` and 1 other fields	High correlation
`cant_apercibimientos` is highly overall correlated with `cant_MontoLimite` and 1 other fields	High correlation
`cant_autenticado` is highly overall correlated with `cant_representante` and 1 other fields	High correlation
`cant_noAutenticado` is highly overall correlated with `cant_Apoderado` and 3 other fields	High correlation
`cant_procesos_adjudicado` is highly overall correlated with `monto_total_adjudicado`	High correlation
`cant_representante` is highly overall correlated with `cant_MontoLimite` and 1 other fields	High correlation
`cant_sinMontoLimite` is highly overall correlated with `cant_Apoderado` and 2 other fields	High correlation
`cant_suspensiones` is highly overall correlated with `cant_MontoLimite` and 1 other fields	High correlation
`dcant_procesos_adjudicado` is highly overall correlated with `cant_MontoLimite`	High correlation
`dmonto_total_adjudicado` is highly overall correlated with `cant_MontoLimite`	High correlation
`dtotal_articulos_provee` is highly overall correlated with `cant_noAutenticado`	High correlation
`monto_total_adjudicado` is highly overall correlated with `cant_MontoLimite` and 1 other fields	High correlation
`periodo_preinscripcion` is highly overall correlated with `anio_preinscripcion` and 1 other fields	High correlation
`total_articulos_provee` is highly overall correlated with `cant_MontoLimite`	High correlation
`Estado` is highly imbalanced (67.0%)	Imbalance
`provincia` is highly imbalanced (53.1%)	Imbalance
`cant_apercibimientos` is highly imbalanced (74.5%)	Imbalance
`cant_Apoderado` is highly imbalanced (61.2%)	Imbalance
`cant_representante` is highly imbalanced (60.9%)	Imbalance
`cant_autenticado` is highly imbalanced (66.5%)	Imbalance
`cant_noAutenticado` is highly imbalanced (54.7%)	Imbalance
`cant_socios` has 55 (23.8%) missing values	Missing
`cant_apercibimientos` has 91 (39.4%) missing values	Missing
`cant_suspensiones` has 117 (50.6%) missing values	Missing
`cant_antecedentes` has 7 (3.0%) missing values	Missing
`cant_Apoderado` has 66 (28.6%) missing values	Missing
`cant_representante` has 120 (51.9%) missing values	Missing
`cant_noAutenticado` has 186 (80.5%) missing values	Missing
`cant_MontoLimite` has 225 (97.4%) missing values	Missing
`CUIT` has unique values	Unique

Reproduction

Analysis started	2025-06-30 15:01:35.304413
Analysis finished	2025-06-30 15:01:41.529085
Duration	6.22 seconds
Software version	ydata-profiling vv4.16.1
Download configuration	config.json

CUIT
Text

Unique

Distinct	231
Distinct (%)	100.0%
Missing	0
Missing (%)	0.0%
Memory size	17.1 KiB

Length

Max length	12
Median length	11
Mean length	10.995671
Min length	9

Characters and Unicode

Total characters	2540
Distinct characters	11
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	231 ?
Unique (%)	100.0%

Sample

1st row	30711500363
2nd row	30590151013
3rd row	30678561165
4th row	30591267759
5th row	30702024834

Value	Count	Frequency (%)
30711500363	1	0.4%
30590151013	1	0.4%
30678561165	1	0.4%
30591267759	1	0.4%
30702024834	1	0.4%
30623295946	1	0.4%
30673249902	1	0.4%
30714236888	1	0.4%
30710362218	1	0.4%
30710828608	1	0.4%
Other values (221)	221	95.7%

Most occurring characters

Value	Count	Frequency (%)
0	430	16.9%
3	353	13.9%
7	287	11.3%
2	272	10.7%
1	235	9.3%
6	217	8.5%
9	197	7.8%
5	193	7.6%
8	186	7.3%
4	169	6.7%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	2540	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
0	430	16.9%
3	353	13.9%
7	287	11.3%
2	272	10.7%
1	235	9.3%
6	217	8.5%
9	197	7.8%
5	193	7.6%
8	186	7.3%
4	169	6.7%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	2540	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
0	430	16.9%
3	353	13.9%
7	287	11.3%
2	272	10.7%
1	235	9.3%
6	217	8.5%
9	197	7.8%
5	193	7.6%
8	186	7.3%
4	169	6.7%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	2540	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
0	430	16.9%
3	353	13.9%
7	287	11.3%
2	272	10.7%
1	235	9.3%
6	217	8.5%
9	197	7.8%
5	193	7.6%
8	186	7.3%
4	169	6.7%

Nombre
Text

Distinct	226
Distinct (%)	97.8%
Missing	0
Missing (%)	0.0%
Memory size	19.4 KiB

Length

Max length	67
Median length	34
Mean length	18.549784
Min length	3

Characters and Unicode

Total characters	4285
Distinct characters	65
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	225 ?
Unique (%)	97.4%

Sample

1st row	LICICOM S.R.L.
2nd row	VIDITEC S.A..
3rd row	NACION SEGUROS S.A.
4th row	ERRE-DE SRL
5th row	Datastar Argentina S.A.

Value	Count	Frequency (%)
s.a	62	9.4%
srl	39	5.9%
sa	27	4.1%
s.r.l	24	3.6%
argentina	15	2.3%
y	12	1.8%
de	11	1.7%
sin	6	0.9%
datos	6	0.9%
a	6	0.9%
Other values (401)	451	68.4%

Most occurring characters

Value	Count	Frequency (%)
	428	10.0%
A	357	8.3%
S	313	7.3%
R	270	6.3%
E	241	5.6%
I	236	5.5%
.	207	4.8%
O	177	4.1%
N	171	4.0%
L	158	3.7%
Other values (55)	1727	40.3%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	4285	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	428	10.0%
A	357	8.3%
S	313	7.3%
R	270	6.3%
E	241	5.6%
I	236	5.5%
.	207	4.8%
O	177	4.1%
N	171	4.0%
L	158	3.7%
Other values (55)	1727	40.3%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	4285	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	428	10.0%
A	357	8.3%
S	313	7.3%
R	270	6.3%
E	241	5.6%
I	236	5.5%
.	207	4.8%
O	177	4.1%
N	171	4.0%
L	158	3.7%
Other values (55)	1727	40.3%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	4285	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	428	10.0%
A	357	8.3%
S	313	7.3%
R	270	6.3%
E	241	5.6%
I	236	5.5%
.	207	4.8%
O	177	4.1%
N	171	4.0%
L	158	3.7%
Other values (55)	1727	40.3%

FechaPreinscripcion
Date

Distinct	173
Distinct (%)	74.9%
Missing	0
Missing (%)	0.0%
Memory size	3.6 KiB

Minimum	2016-02-08 00:00:00
Maximum	2022-02-20 00:00:00
Invalid dates	0
Invalid dates (%)	0.0%

Histogram with fixed size bins (bins=50)

Estado
Categorical

High correlation Imbalance

Distinct	8
Distinct (%)	3.5%
Missing	0
Missing (%)	0.0%
Memory size	17.4 KiB

Inscripto	196
Desactualizado doc. vencidos	11
Suspendido	6
Con Solicitud De Baja	6
Desactualizado mantención	6
Other values (3)	6

Length

Max length	28
Median length	9
Mean length	10.809524
Min length	9

Characters and Unicode

Total characters	2497
Distinct characters	27
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	2 ?
Unique (%)	0.9%

Sample

1st row	Inscripto
2nd row	Inscripto
3rd row	Inscripto
4th row	Inscripto
5th row	Inscripto

Common Values

Value	Count	Frequency (%)
Inscripto	196	84.8%
Desactualizado doc. vencidos	11	4.8%
Suspendido	6	2.6%
Con Solicitud De Baja	6	2.6%
Desactualizado mantención	6	2.6%
Pre Inscripto	4	1.7%
En Evaluacion	1	0.4%
Desactualizado Por Clase	1	0.4%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
inscripto	200	70.4%
desactualizado	18	6.3%
doc	11	3.9%
vencidos	11	3.9%
suspendido	6	2.1%
con	6	2.1%
solicitud	6	2.1%
de	6	2.1%
baja	6	2.1%
mantención	6	2.1%
Other values (5)	8	2.8%

Most occurring characters

Value	Count	Frequency (%)
o	260	10.4%
i	254	10.2%
c	253	10.1%
n	243	9.7%
s	236	9.5%
t	230	9.2%
p	206	8.2%
r	205	8.2%
I	200	8.0%
a	75	3.0%
Other values (17)	335	13.4%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	2497	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
o	260	10.4%
i	254	10.2%
c	253	10.1%
n	243	9.7%
s	236	9.5%
t	230	9.2%
p	206	8.2%
r	205	8.2%
I	200	8.0%
a	75	3.0%
Other values (17)	335	13.4%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	2497	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
o	260	10.4%
i	254	10.2%
c	253	10.1%
n	243	9.7%
s	236	9.5%
t	230	9.2%
p	206	8.2%
r	205	8.2%
I	200	8.0%
a	75	3.0%
Other values (17)	335	13.4%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	2497	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
o	260	10.4%
i	254	10.2%
c	253	10.1%
n	243	9.7%
s	236	9.5%
t	230	9.2%
p	206	8.2%
r	205	8.2%
I	200	8.0%
a	75	3.0%
Other values (17)	335	13.4%

TipoSocietario
Categorical

Distinct	8
Distinct (%)	3.5%
Missing	0
Missing (%)	0.0%
Memory size	23.5 KiB

Sociedad Anónima	95
S.R.L	67
Persona Física	54
Otras Formas Societarias	7
Sociedades De Hecho	3
Other values (3)	5

Length

Max length	26
Median length	24
Mean length	12.74026
Min length	5

Characters and Unicode

Total characters	2943
Distinct characters	36
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	2 ?
Unique (%)	0.9%

Sample

1st row	S.R.L
2nd row	Sociedad Anónima
3rd row	Sociedad Anónima
4th row	S.R.L
5th row	Sociedad Anónima

Common Values

Value	Count	Frequency (%)
Sociedad Anónima	95	41.1%
S.R.L	67	29.0%
Persona Física	54	23.4%
Otras Formas Societarias	7	3.0%
Sociedades De Hecho	3	1.3%
PJ Extranjero Sin Sucursal	3	1.3%
Cooperativas	1	0.4%
Organismo Publico	1	0.4%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
sociedad	95	23.2%
anónima	95	23.2%
s.r.l	67	16.3%
persona	54	13.2%
física	54	13.2%
otras	7	1.7%
formas	7	1.7%
societarias	7	1.7%
sociedades	3	0.7%
de	3	0.7%
Other values (8)	18	4.4%

Most occurring characters

Value	Count	Frequency (%)
a	338	11.5%
i	267	9.1%
n	251	8.5%
d	196	6.7%
	179	6.1%
S	178	6.0%
o	176	6.0%
e	172	5.8%
c	166	5.6%
s	137	4.7%
Other values (26)	883	30.0%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	2943	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
a	338	11.5%
i	267	9.1%
n	251	8.5%
d	196	6.7%
	179	6.1%
S	178	6.0%
o	176	6.0%
e	172	5.8%
c	166	5.6%
s	137	4.7%
Other values (26)	883	30.0%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	2943	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
a	338	11.5%
i	267	9.1%
n	251	8.5%
d	196	6.7%
	179	6.1%
S	178	6.0%
o	176	6.0%
e	172	5.8%
c	166	5.6%
s	137	4.7%
Other values (26)	883	30.0%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	2943	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
a	338	11.5%
i	267	9.1%
n	251	8.5%
d	196	6.7%
	179	6.1%
S	178	6.0%
o	176	6.0%
e	172	5.8%
c	166	5.6%
s	137	4.7%
Other values (26)	883	30.0%

periodo_preinscripcion
Real number (ℝ)

High correlation

Distinct	43
Distinct (%)	18.6%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	201694.48

Minimum	201607
Maximum	202202
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	3.6 KiB

Quantile statistics

Minimum	201607
5-th percentile	201608
Q1	201611
median	201701
Q3	201707.5
95-th percentile	201905
Maximum	202202
Range	595
Interquartile range (IQR)	96.5

Descriptive statistics

Standard deviation	97.591644
Coefficient of variation (CV)	0.00048385877
Kurtosis	4.4879017
Mean	201694.48
Median Absolute Deviation (MAD)	90
Skewness	1.7293275
Sum	46591425
Variance	9524.129
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=43)

Value	Count	Frequency (%)
201611	27	11.7%
201701	25	10.8%
201610	22	9.5%
201612	19	8.2%
201609	14	6.1%
201703	14	6.1%
201702	11	4.8%
201704	11	4.8%
201608	9	3.9%
201705	8	3.5%
Other values (33)	71	30.7%

Value	Count	Frequency (%)
201607	4	1.7%
201608	9	3.9%
201609	14	6.1%
201610	22	9.5%
201611	27	11.7%
201612	19	8.2%
201701	25	10.8%
201702	11	4.8%
201703	14	6.1%
201704	11	4.8%

Value	Count	Frequency (%)
202202	1	0.4%
202111	1	0.4%
202010	1	0.4%
202009	1	0.4%
202001	1	0.4%
201911	1	0.4%
201910	2	0.9%
201909	1	0.4%
201908	1	0.4%
201907	1	0.4%

anio_preinscripcion
Real number (ℝ)

High correlation

Distinct	7
Distinct (%)	3.0%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	2016.8745

Minimum	2016
Maximum	2022
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	3.6 KiB

Quantile statistics

Minimum	2016
5-th percentile	2016
Q1	2016
median	2017
Q3	2017
95-th percentile	2019
Maximum	2022
Range	6
Interquartile range (IQR)	1

Descriptive statistics

Standard deviation	0.98986013
Coefficient of variation (CV)	0.00049078916
Kurtosis	4.1821911
Mean	2016.8745
Median Absolute Deviation (MAD)	1
Skewness	1.6384554
Sum	465898
Variance	0.97982308
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=7)

Value	Count	Frequency (%)
2016	95	41.1%
2017	92	39.8%
2018	30	13.0%
2019	9	3.9%
2020	3	1.3%
2021	1	0.4%
2022	1	0.4%

Value	Count	Frequency (%)
2016	95	41.1%
2017	92	39.8%
2018	30	13.0%
2019	9	3.9%
2020	3	1.3%
2021	1	0.4%
2022	1	0.4%

Value	Count	Frequency (%)
2022	1	0.4%
2021	1	0.4%
2020	3	1.3%
2019	9	3.9%
2018	30	13.0%
2017	92	39.8%
2016	95	41.1%

cant_procesos_adjudicado
Real number (ℝ)

High correlation

Distinct	111
Distinct (%)	48.5%
Missing	2
Missing (%)	0.9%
Infinite	0
Infinite (%)	0.0%
Mean	100.74672

Minimum	1
Maximum	1214
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	3.6 KiB

Quantile statistics

Minimum	1
5-th percentile	2
Q1	7
median	27
Q3	82
95-th percentile	439.2
Maximum	1214
Range	1213
Interquartile range (IQR)	75

Descriptive statistics

Standard deviation	196.61717
Coefficient of variation (CV)	1.9515987
Kurtosis	12.227338
Mean	100.74672
Median Absolute Deviation (MAD)	24
Skewness	3.3737497
Sum	23071
Variance	38658.313
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
3	18	7.8%
1	11	4.8%
2	10	4.3%
13	8	3.5%
5	7	3.0%
22	7	3.0%
6	5	2.2%
8	5	2.2%
4	5	2.2%
48	4	1.7%
Other values (101)	149	64.5%

Value	Count	Frequency (%)
1	11	4.8%
2	10	4.3%
3	18	7.8%
4	5	2.2%
5	7	3.0%
6	5	2.2%
7	3	1.3%
8	5	2.2%
9	1	0.4%
10	2	0.9%

Value	Count	Frequency (%)
1214	1	0.4%
1102	1	0.4%
989	1	0.4%
895	1	0.4%
889	1	0.4%
864	1	0.4%
804	1	0.4%
792	1	0.4%
649	1	0.4%
635	1	0.4%

monto_total_adjudicado
Real number (ℝ)

High correlation

Distinct	229
Distinct (%)	100.0%
Missing	2
Missing (%)	0.9%
Infinite	0
Infinite (%)	0.0%
Mean	2.387313 × 10⁸

Minimum	6872
Maximum	6.9338191 × 10⁹
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	3.6 KiB

Quantile statistics

Minimum	6872
5-th percentile	518307.71
Q1	6445227.6
median	33810393
Q3	1.6714006 × 10⁸
95-th percentile	8.2985888 × 10⁸
Maximum	6.9338191 × 10⁹
Range	6.9338122 × 10⁹
Interquartile range (IQR)	1.6069483 × 10⁸

Descriptive statistics

Standard deviation	7.2642602 × 10⁸
Coefficient of variation (CV)	3.0428604
Kurtosis	49.438449
Mean	2.387313 × 10⁸
Median Absolute Deviation (MAD)	32548404
Skewness	6.4804021
Sum	5.4669467 × 10¹⁰
Variance	5.2769476 × 10¹⁷
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
7425067.382	1	0.4%
6933819091	1	0.4%
137988939	1	0.4%
3182253860	1	0.4%
27146231.22	1	0.4%
1375720032	1	0.4%
7242920.713	1	0.4%
86070142.1	1	0.4%
64475958.4	1	0.4%
474560333	1	0.4%
Other values (219)	219	94.8%
(Missing)	2	0.9%

Value	Count	Frequency (%)
6872	1	0.4%
23910.30243	1	0.4%
42000	1	0.4%
84943.48	1	0.4%
100200	1	0.4%
112000	1	0.4%
132004.6361	1	0.4%
213056.6512	1	0.4%
240550.6182	1	0.4%
354057.6923	1	0.4%

Value	Count	Frequency (%)
6933819091	1	0.4%
5903229493	1	0.4%
3182253860	1	0.4%
3132430594	1	0.4%
2548401882	1	0.4%
2252733473	1	0.4%
2092322557	1	0.4%
1375720032	1	0.4%
1375349531	1	0.4%
943321354.4	1	0.4%

antiguedad
Real number (ℝ)

High correlation

Distinct	6
Distinct (%)	2.6%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	4.1298701

Minimum	0
Maximum	5
Zeros	2
Zeros (%)	0.9%
Negative	0
Negative (%)	0.0%
Memory size	3.6 KiB

Quantile statistics

Minimum	0
5-th percentile	2
Q1	4
median	4
Q3	5
95-th percentile	5
Maximum	5
Range	5
Interquartile range (IQR)	1

Descriptive statistics

Standard deviation	0.96932046
Coefficient of variation (CV)	0.23470967
Kurtosis	2.8156944
Mean	4.1298701
Median Absolute Deviation (MAD)	1
Skewness	-1.4480491
Sum	954
Variance	0.93958216
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=6)

Value	Count	Frequency (%)
5	95	41.1%
4	92	39.8%
3	30	13.0%
2	9	3.9%
1	3	1.3%
0	2	0.9%

Value	Count	Frequency (%)
0	2	0.9%
1	3	1.3%
2	9	3.9%
3	30	13.0%
4	92	39.8%
5	95	41.1%

Value	Count	Frequency (%)
5	95	41.1%
4	92	39.8%
3	30	13.0%
2	9	3.9%
1	3	1.3%
0	2	0.9%

provincia
Categorical

Imbalance

Distinct	15
Distinct (%)	6.5%
Missing	0
Missing (%)	0.0%
Memory size	16.5 KiB

CABA	132
Buenos Aires	66
Santa Fe	9
Córdoba	7
San Juan	3
Other values (10)	14

Length

Max length	12
Median length	4
Mean length	6.8701299
Min length	4

Characters and Unicode

Total characters	1587
Distinct characters	33
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	7 ?
Unique (%)	3.0%

Sample

1st row	CABA
2nd row	CABA
3rd row	CABA
4th row	Buenos Aires
5th row	CABA

Common Values

Value	Count	Frequency (%)
CABA	132	57.1%
Buenos Aires	66	28.6%
Santa Fe	9	3.9%
Córdoba	7	3.0%
San Juan	3	1.3%
Extranjera	3	1.3%
San Luis	2	0.9%
Corrientes	2	0.9%
Rio Negro	1	0.4%
La Rioja	1	0.4%
Other values (5)	5	2.2%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
caba	132	42.0%
buenos	66	21.0%
aires	66	21.0%
santa	9	2.9%
fe	9	2.9%
córdoba	7	2.2%
san	5	1.6%
juan	3	1.0%
extranjera	3	1.0%
luis	2	0.6%
Other values (11)	12	3.8%

Most occurring characters

Value	Count	Frequency (%)
A	330	20.8%
B	198	12.5%
e	151	9.5%
C	143	9.0%
s	137	8.6%
n	91	5.7%
r	86	5.4%
	83	5.2%
o	80	5.0%
u	75	4.7%
Other values (23)	213	13.4%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	1587	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
A	330	20.8%
B	198	12.5%
e	151	9.5%
C	143	9.0%
s	137	8.6%
n	91	5.7%
r	86	5.4%
	83	5.2%
o	80	5.0%
u	75	4.7%
Other values (23)	213	13.4%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	1587	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
A	330	20.8%
B	198	12.5%
e	151	9.5%
C	143	9.0%
s	137	8.6%
n	91	5.7%
r	86	5.4%
	83	5.2%
o	80	5.0%
u	75	4.7%
Other values (23)	213	13.4%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	1587	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
A	330	20.8%
B	198	12.5%
e	151	9.5%
C	143	9.0%
s	137	8.6%
n	91	5.7%
r	86	5.4%
	83	5.2%
o	80	5.0%
u	75	4.7%
Other values (23)	213	13.4%

cant_socios
Categorical

Missing

Distinct	5
Distinct (%)	2.8%
Missing	55
Missing (%)	23.8%
Memory size	15.1 KiB

2.0	79
1.0	68
3.0	17
4.0	7
5.0	5

Length

Max length	3
Median length	3
Mean length	3
Min length	3

Characters and Unicode

Total characters	528
Distinct characters	7
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	2.0
2nd row	5.0
3rd row	5.0
4th row	2.0
5th row	2.0

Common Values

Value	Count	Frequency (%)
2.0	79	34.2%
1.0	68	29.4%
3.0	17	7.4%
4.0	7	3.0%
5.0	5	2.2%
(Missing)	55	23.8%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
2.0	79	44.9%
1.0	68	38.6%
3.0	17	9.7%
4.0	7	4.0%
5.0	5	2.8%

Most occurring characters

Value	Count	Frequency (%)
.	176	33.3%
0	176	33.3%
2	79	15.0%
1	68	12.9%
3	17	3.2%
4	7	1.3%
5	5	0.9%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	528	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
.	176	33.3%
0	176	33.3%
2	79	15.0%
1	68	12.9%
3	17	3.2%
4	7	1.3%
5	5	0.9%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	528	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
.	176	33.3%
0	176	33.3%
2	79	15.0%
1	68	12.9%
3	17	3.2%
4	7	1.3%
5	5	0.9%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	528	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
.	176	33.3%
0	176	33.3%
2	79	15.0%
1	68	12.9%
3	17	3.2%
4	7	1.3%
5	5	0.9%

cant_apercibimientos
Categorical

High correlation Imbalance Missing

Distinct	3
Distinct (%)	2.1%
Missing	91
Missing (%)	39.4%
Memory size	15.0 KiB

1.0	130
2.0	9
3.0	1

Length

Max length	3
Median length	3
Mean length	3
Min length	3

Characters and Unicode

Total characters	420
Distinct characters	5
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	1 ?
Unique (%)	0.7%

Sample

1st row	1.0
2nd row	1.0
3rd row	1.0
4th row	1.0
5th row	1.0

Common Values

Value	Count	Frequency (%)
1.0	130	56.3%
2.0	9	3.9%
3.0	1	0.4%
(Missing)	91	39.4%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
1.0	130	92.9%
2.0	9	6.4%
3.0	1	0.7%

Most occurring characters

Value	Count	Frequency (%)
.	140	33.3%
0	140	33.3%
1	130	31.0%
2	9	2.1%
3	1	0.2%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	420	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
.	140	33.3%
0	140	33.3%
1	130	31.0%
2	9	2.1%
3	1	0.2%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	420	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
.	140	33.3%
0	140	33.3%
1	130	31.0%
2	9	2.1%
3	1	0.2%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	420	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
.	140	33.3%
0	140	33.3%
1	130	31.0%
2	9	2.1%
3	1	0.2%

cant_suspensiones
Real number (ℝ)

High correlation Missing

Distinct	7
Distinct (%)	6.1%
Missing	117
Missing (%)	50.6%
Infinite	0
Infinite (%)	0.0%
Mean	1.8333333

Minimum	1
Maximum	7
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	3.6 KiB

Quantile statistics

Minimum	1
5-th percentile	1
Q1	1
median	2
Q3	2
95-th percentile	4
Maximum	7
Range	6
Interquartile range (IQR)	1

Descriptive statistics

Standard deviation	1.1124132
Coefficient of variation (CV)	0.60677084
Kurtosis	6.4372696
Mean	1.8333333
Median Absolute Deviation (MAD)	1
Skewness	2.2604151
Sum	209
Variance	1.2374631
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=7)

Value	Count	Frequency (%)
1	51	22.1%
2	48	20.8%
3	6	2.6%
4	5	2.2%
6	2	0.9%
5	1	0.4%
7	1	0.4%
(Missing)	117	50.6%

Value	Count	Frequency (%)
1	51	22.1%
2	48	20.8%
3	6	2.6%
4	5	2.2%
5	1	0.4%
6	2	0.9%
7	1	0.4%

Value	Count	Frequency (%)
7	1	0.4%
6	2	0.9%
5	1	0.4%
4	5	2.2%
3	6	2.6%
2	48	20.8%
1	51	22.1%

cant_antecedentes
Real number (ℝ)

High correlation Missing

Distinct	8
Distinct (%)	3.6%
Missing	7
Missing (%)	3.0%
Infinite	0
Infinite (%)	0.0%
Mean	1.7276786

Minimum	1
Maximum	8
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	3.6 KiB

Quantile statistics

Minimum	1
5-th percentile	1
Q1	1
median	1
Q3	2
95-th percentile	4
Maximum	8
Range	7
Interquartile range (IQR)	1

Descriptive statistics

Standard deviation	1.1644749
Coefficient of variation (CV)	0.6740113
Kurtosis	7.1487526
Mean	1.7276786
Median Absolute Deviation (MAD)	0
Skewness	2.4200953
Sum	387
Variance	1.3560018
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=8)

Value	Count	Frequency (%)
1	127	55.0%
2	65	28.1%
3	15	6.5%
4	8	3.5%
5	4	1.7%
6	3	1.3%
7	1	0.4%
8	1	0.4%
(Missing)	7	3.0%

Value	Count	Frequency (%)
1	127	55.0%
2	65	28.1%
3	15	6.5%
4	8	3.5%
5	4	1.7%
6	3	1.3%
7	1	0.4%
8	1	0.4%

Value	Count	Frequency (%)
8	1	0.4%
7	1	0.4%
6	3	1.3%
5	4	1.7%
4	8	3.5%
3	15	6.5%
2	65	28.1%
1	127	55.0%

cant_Apoderado
Categorical

High correlation Imbalance Missing

Distinct	5
Distinct (%)	3.0%
Missing	66
Missing (%)	28.6%
Memory size	15.1 KiB

1.0	136
2.0	20
3.0	5
4.0	3
5.0	1

Length

Max length	3
Median length	3
Mean length	3
Min length	3

Characters and Unicode

Total characters	495
Distinct characters	7
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	1 ?
Unique (%)	0.6%

Sample

1st row	2.0
2nd row	1.0
3rd row	1.0
4th row	3.0
5th row	2.0

Common Values

Value	Count	Frequency (%)
1.0	136	58.9%
2.0	20	8.7%
3.0	5	2.2%
4.0	3	1.3%
5.0	1	0.4%
(Missing)	66	28.6%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
1.0	136	82.4%
2.0	20	12.1%
3.0	5	3.0%
4.0	3	1.8%
5.0	1	0.6%

Most occurring characters

Value	Count	Frequency (%)
.	165	33.3%
0	165	33.3%
1	136	27.5%
2	20	4.0%
3	5	1.0%
4	3	0.6%
5	1	0.2%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	495	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
.	165	33.3%
0	165	33.3%
1	136	27.5%
2	20	4.0%
3	5	1.0%
4	3	0.6%
5	1	0.2%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	495	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
.	165	33.3%
0	165	33.3%
1	136	27.5%
2	20	4.0%
3	5	1.0%
4	3	0.6%
5	1	0.2%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	495	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
.	165	33.3%
0	165	33.3%
1	136	27.5%
2	20	4.0%
3	5	1.0%
4	3	0.6%
5	1	0.2%

cant_representante
Categorical

High correlation Imbalance Missing

Distinct	3
Distinct (%)	2.7%
Missing	120
Missing (%)	51.9%
Memory size	14.9 KiB

1.0	96
2.0	14
3.0	1

Length

Max length	3
Median length	3
Mean length	3
Min length	3

Characters and Unicode

Total characters	333
Distinct characters	5
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	1 ?
Unique (%)	0.9%

Sample

1st row	1.0
2nd row	2.0
3rd row	2.0
4th row	1.0
5th row	2.0

Common Values

Value	Count	Frequency (%)
1.0	96	41.6%
2.0	14	6.1%
3.0	1	0.4%
(Missing)	120	51.9%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
1.0	96	86.5%
2.0	14	12.6%
3.0	1	0.9%

Most occurring characters

Value	Count	Frequency (%)
.	111	33.3%
0	111	33.3%
1	96	28.8%
2	14	4.2%
3	1	0.3%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	333	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
.	111	33.3%
0	111	33.3%
1	96	28.8%
2	14	4.2%
3	1	0.3%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	333	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
.	111	33.3%
0	111	33.3%
1	96	28.8%
2	14	4.2%
3	1	0.3%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	333	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
.	111	33.3%
0	111	33.3%
1	96	28.8%
2	14	4.2%
3	1	0.3%

cant_autenticado
Categorical

High correlation Imbalance

Distinct	5
Distinct (%)	2.2%
Missing	0
Missing (%)	0.0%
Memory size	15.3 KiB

1.0	192
2.0	34
3.0	3
4.0	1
5.0	1

Length

Max length	3
Median length	3
Mean length	3
Min length	3

Characters and Unicode

Total characters	693
Distinct characters	7
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	2 ?
Unique (%)	0.9%

Sample

1st row	2.0
2nd row	1.0
3rd row	1.0
4th row	1.0
5th row	2.0

Common Values

Value	Count	Frequency (%)
1.0	192	83.1%
2.0	34	14.7%
3.0	3	1.3%
4.0	1	0.4%
5.0	1	0.4%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
1.0	192	83.1%
2.0	34	14.7%
3.0	3	1.3%
4.0	1	0.4%
5.0	1	0.4%

Most occurring characters

Value	Count	Frequency (%)
.	231	33.3%
0	231	33.3%
1	192	27.7%
2	34	4.9%
3	3	0.4%
4	1	0.1%
5	1	0.1%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	693	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
.	231	33.3%
0	231	33.3%
1	192	27.7%
2	34	4.9%
3	3	0.4%
4	1	0.1%
5	1	0.1%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	693	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
.	231	33.3%
0	231	33.3%
1	192	27.7%
2	34	4.9%
3	3	0.4%
4	1	0.1%
5	1	0.1%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	693	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
.	231	33.3%
0	231	33.3%
1	192	27.7%
2	34	4.9%
3	3	0.4%
4	1	0.1%
5	1	0.1%

cant_noAutenticado
Categorical

High correlation Imbalance Missing

Distinct	4
Distinct (%)	8.9%
Missing	186
Missing (%)	80.5%
Memory size	14.6 KiB

1.0	37
2.0	5
3.0	2
4.0	1

Length

Max length	3
Median length	3
Mean length	3
Min length	3

Characters and Unicode

Total characters	135
Distinct characters	6
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	1 ?
Unique (%)	2.2%

Sample

1st row	1.0
2nd row	2.0
3rd row	2.0
4th row	1.0
5th row	1.0

Common Values

Value	Count	Frequency (%)
1.0	37	16.0%
2.0	5	2.2%
3.0	2	0.9%
4.0	1	0.4%
(Missing)	186	80.5%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
1.0	37	82.2%
2.0	5	11.1%
3.0	2	4.4%
4.0	1	2.2%

Most occurring characters

Value	Count	Frequency (%)
.	45	33.3%
0	45	33.3%
1	37	27.4%
2	5	3.7%
3	2	1.5%
4	1	0.7%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	135	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
.	45	33.3%
0	45	33.3%
1	37	27.4%
2	5	3.7%
3	2	1.5%
4	1	0.7%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	135	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
.	45	33.3%
0	45	33.3%
1	37	27.4%
2	5	3.7%
3	2	1.5%
4	1	0.7%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	135	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
.	45	33.3%
0	45	33.3%
1	37	27.4%
2	5	3.7%
3	2	1.5%
4	1	0.7%

cant_sinMontoLimite
Categorical

High correlation

Distinct	5
Distinct (%)	2.2%
Missing	1
Missing (%)	0.4%
Memory size	15.3 KiB

1.0	158
2.0	57
3.0	7
4.0	5
5.0	3

Length

Max length	3
Median length	3
Mean length	3
Min length	3

Characters and Unicode

Total characters	690
Distinct characters	7
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	3.0
2nd row	1.0
3rd row	1.0
4th row	2.0
5th row	4.0

Common Values

Value	Count	Frequency (%)
1.0	158	68.4%
2.0	57	24.7%
3.0	7	3.0%
4.0	5	2.2%
5.0	3	1.3%
(Missing)	1	0.4%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
1.0	158	68.7%
2.0	57	24.8%
3.0	7	3.0%
4.0	5	2.2%
5.0	3	1.3%

Most occurring characters

Value	Count	Frequency (%)
.	230	33.3%
0	230	33.3%
1	158	22.9%
2	57	8.3%
3	7	1.0%
4	5	0.7%
5	3	0.4%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	690	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
.	230	33.3%
0	230	33.3%
1	158	22.9%
2	57	8.3%
3	7	1.0%
4	5	0.7%
5	3	0.4%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	690	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
.	230	33.3%
0	230	33.3%
1	158	22.9%
2	57	8.3%
3	7	1.0%
4	5	0.7%
5	3	0.4%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	690	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
.	230	33.3%
0	230	33.3%
1	158	22.9%
2	57	8.3%
3	7	1.0%
4	5	0.7%
5	3	0.4%

cant_MontoLimite
Categorical

High correlation Missing

Distinct	2
Distinct (%)	33.3%
Missing	225
Missing (%)	97.4%
Memory size	14.5 KiB

1.0	5
2.0	1

Length

Max length	3
Median length	3
Mean length	3
Min length	3

Characters and Unicode

Total characters	18
Distinct characters	4
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	1 ?
Unique (%)	16.7%

Sample

1st row	1.0
2nd row	2.0
3rd row	1.0
4th row	1.0
5th row	1.0

Common Values

Value	Count	Frequency (%)
1.0	5	2.2%
2.0	1	0.4%
(Missing)	225	97.4%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
1.0	5	83.3%
2.0	1	16.7%

Most occurring characters

Value	Count	Frequency (%)
.	6	33.3%
0	6	33.3%
1	5	27.8%
2	1	5.6%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	18	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
.	6	33.3%
0	6	33.3%
1	5	27.8%
2	1	5.6%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	18	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
.	6	33.3%
0	6	33.3%
1	5	27.8%
2	1	5.6%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	18	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
.	6	33.3%
0	6	33.3%
1	5	27.8%
2	1	5.6%

total_articulos_provee
Real number (ℝ)

High correlation

Distinct	163
Distinct (%)	70.6%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	461.66667

Minimum	1
Maximum	6993
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	3.6 KiB

Quantile statistics

Minimum	1
5-th percentile	2.5
Q1	25
median	76
Q3	304.5
95-th percentile	3159.5
Maximum	6993
Range	6992
Interquartile range (IQR)	279.5

Descriptive statistics

Standard deviation	1140.3776
Coefficient of variation (CV)	2.4701321
Kurtosis	15.386786
Mean	461.66667
Median Absolute Deviation (MAD)	67
Skewness	3.8669714
Sum	106645
Variance	1300461.2
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
1	11	4.8%
5	6	2.6%
31	5	2.2%
47	4	1.7%
3	4	1.7%
30	4	1.7%
105	3	1.3%
9	3	1.3%
37	3	1.3%
46	3	1.3%
Other values (153)	185	80.1%

Value	Count	Frequency (%)
1	11	4.8%
2	1	0.4%
3	4	1.7%
4	3	1.3%
5	6	2.6%
6	3	1.3%
7	2	0.9%
8	1	0.4%
9	3	1.3%
10	1	0.4%

Value	Count	Frequency (%)
6993	1	0.4%
6661	1	0.4%
6064	1	0.4%
5765	1	0.4%
5612	1	0.4%
4867	1	0.4%
4471	1	0.4%
3956	1	0.4%
3686	1	0.4%
3605	1	0.4%

dmonto_total_adjudicado
Categorical

High correlation

Distinct	21
Distinct (%)	9.1%
Missing	0
Missing (%)	0.0%
Memory size	20.0 KiB

(222.964.579- 46.172.150.151]	47
(89.439.449- 222.964.579]	33
(46.718.747- 89.439.449]	28
(19.975.532- 30.451.916]	20
(6.702.697- 9.424.898]	14
Other values (16)	89

Length

Max length	29
Median length	24
Mean length	23.87013
Min length	3

Characters and Unicode

Total characters	5514
Distinct characters	17
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	(6.702.697- 9.424.898]
2nd row	(19.975.532- 30.451.916]
3rd row	(222.964.579- 46.172.150.151]
4th row	(89.439.449- 222.964.579]
5th row	(222.964.579- 46.172.150.151]

Common Values

Value	Count	Frequency (%)
(222.964.579- 46.172.150.151]	47	20.3%
(89.439.449- 222.964.579]	33	14.3%
(46.718.747- 89.439.449]	28	12.1%
(19.975.532- 30.451.916]	20	8.7%
(6.702.697- 9.424.898]	14	6.1%
(13.557.176- 19.975.532]	12	5.2%
(2.483.085- 3.396.600]	9	3.9%
(9.424.898- 13.557.176]	9	3.9%
(4.727.330- 6.702.697]	8	3.5%
(30.451.916- 46.718.747]	8	3.5%
Other values (11)	43	18.6%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
222.964.579	80	17.4%
89.439.449	61	13.3%
46.172.150.151	47	10.2%
46.718.747	36	7.8%
19.975.532	32	7.0%
30.451.916	28	6.1%
9.424.898	23	5.0%
6.702.697	22	4.8%
13.557.176	21	4.6%
2.483.085	15	3.3%
Other values (12)	95	20.7%

Most occurring characters

Value	Count	Frequency (%)
.	917	16.6%
9	567	10.3%
4	496	9.0%
7	450	8.2%
2	423	7.7%
1	390	7.1%
5	354	6.4%
6	320	5.8%
3	266	4.8%
-	231	4.2%
Other values (7)	1100	19.9%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	5514	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
.	917	16.6%
9	567	10.3%
4	496	9.0%
7	450	8.2%
2	423	7.7%
1	390	7.1%
5	354	6.4%
6	320	5.8%
3	266	4.8%
-	231	4.2%
Other values (7)	1100	19.9%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	5514	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
.	917	16.6%
9	567	10.3%
4	496	9.0%
7	450	8.2%
2	423	7.7%
1	390	7.1%
5	354	6.4%
6	320	5.8%
3	266	4.8%
-	231	4.2%
Other values (7)	1100	19.9%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	5514	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
.	917	16.6%
9	567	10.3%
4	496	9.0%
7	450	8.2%
2	423	7.7%
1	390	7.1%
5	354	6.4%
6	320	5.8%
3	266	4.8%
-	231	4.2%
Other values (7)	1100	19.9%

dcant_procesos_adjudicado
Categorical

High correlation

Distinct	10
Distinct (%)	4.4%
Missing	2
Missing (%)	0.9%
Memory size	17.4 KiB

(39.0, 1214.0]	98
(19.0, 39.0]	35
(12.0, 19.0]	23
(0.999, 2.0]	21
(2.0, 3.0]	18
Other values (5)	34

Length

Max length	14
Median length	12
Mean length	12.441048
Min length	10

Characters and Unicode

Total characters	2849
Distinct characters	14
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	(19.0, 39.0]
2nd row	(39.0, 1214.0]
3rd row	(39.0, 1214.0]
4th row	(39.0, 1214.0]
5th row	(39.0, 1214.0]

Common Values

Value	Count	Frequency (%)
(39.0, 1214.0]	98	42.4%
(19.0, 39.0]	35	15.2%
(12.0, 19.0]	23	10.0%
(0.999, 2.0]	21	9.1%
(2.0, 3.0]	18	7.8%
(8.0, 12.0]	9	3.9%
(6.0, 8.0]	8	3.5%
(4.0, 5.0]	7	3.0%
(5.0, 6.0]	5	2.2%
(3.0, 4.0]	5	2.2%
(Missing)	2	0.9%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
39.0	133	29.0%
1214.0	98	21.4%
19.0	58	12.7%
2.0	39	8.5%
12.0	32	7.0%
3.0	23	5.0%
0.999	21	4.6%
8.0	17	3.7%
6.0	13	2.8%
4.0	12	2.6%

Most occurring characters

Value	Count	Frequency (%)
0	458	16.1%
.	458	16.1%
1	286	10.0%
9	254	8.9%
,	229	8.0%
(	229	8.0%
]	229	8.0%
	229	8.0%
2	169	5.9%
3	156	5.5%
Other values (4)	152	5.3%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	2849	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
0	458	16.1%
.	458	16.1%
1	286	10.0%
9	254	8.9%
,	229	8.0%
(	229	8.0%
]	229	8.0%
	229	8.0%
2	169	5.9%
3	156	5.5%
Other values (4)	152	5.3%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	2849	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
0	458	16.1%
.	458	16.1%
1	286	10.0%
9	254	8.9%
,	229	8.0%
(	229	8.0%
]	229	8.0%
	229	8.0%
2	169	5.9%
3	156	5.5%
Other values (4)	152	5.3%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	2849	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
0	458	16.1%
.	458	16.1%
1	286	10.0%
9	254	8.9%
,	229	8.0%
(	229	8.0%
]	229	8.0%
	229	8.0%
2	169	5.9%
3	156	5.5%
Other values (4)	152	5.3%

dtotal_articulos_provee
Categorical

High correlation

Distinct	15
Distinct (%)	6.5%
Missing	0
Missing (%)	0.0%
Memory size	17.6 KiB

(345.0, 6993.0]	53
(97.6, 161.0]	26
(58.0, 97.6]	25
(161.0, 345.0]	24
(40.0, 58.0]	22
Other values (10)	81

Length

Max length	15
Median length	14
Mean length	12.822511
Min length	10

Characters and Unicode

Total characters	2962
Distinct characters	15
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	(97.6, 161.0]
2nd row	(97.6, 161.0]
3rd row	(21.0, 29.0]
4th row	(161.0, 345.0]
5th row	(161.0, 345.0]

Common Values

Value	Count	Frequency (%)
(345.0, 6993.0]	53	22.9%
(97.6, 161.0]	26	11.3%
(58.0, 97.6]	25	10.8%
(161.0, 345.0]	24	10.4%
(40.0, 58.0]	22	9.5%
(29.0, 40.0]	16	6.9%
(0.999, 2.0]	12	5.2%
(21.0, 29.0]	11	4.8%
(15.0, 21.0]	10	4.3%
(4.0, 6.0]	9	3.9%
Other values (5)	23	10.0%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
345.0	77	16.7%
6993.0	53	11.5%
97.6	51	11.0%
161.0	50	10.8%
58.0	47	10.2%
40.0	38	8.2%
29.0	27	5.8%
21.0	21	4.5%
15.0	18	3.9%
2.0	16	3.5%
Other values (6)	64	13.9%

Most occurring characters

Value	Count	Frequency (%)
.	462	15.6%
0	449	15.2%
(	231	7.8%
,	231	7.8%
	231	7.8%
]	231	7.8%
9	220	7.4%
6	166	5.6%
1	165	5.6%
5	142	4.8%
Other values (5)	434	14.7%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	2962	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
.	462	15.6%
0	449	15.2%
(	231	7.8%
,	231	7.8%
	231	7.8%
]	231	7.8%
9	220	7.4%
6	166	5.6%
1	165	5.6%
5	142	4.8%
Other values (5)	434	14.7%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	2962	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
.	462	15.6%
0	449	15.2%
(	231	7.8%
,	231	7.8%
	231	7.8%
]	231	7.8%
9	220	7.4%
6	166	5.6%
1	165	5.6%
5	142	4.8%
Other values (5)	434	14.7%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	2962	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
.	462	15.6%
0	449	15.2%
(	231	7.8%
,	231	7.8%
	231	7.8%
]	231	7.8%
9	220	7.4%
6	166	5.6%
1	165	5.6%
5	142	4.8%
Other values (5)	434	14.7%

Cluster_6
Categorical

Constant

Distinct	1
Distinct (%)	0.4%
Missing	0
Missing (%)	0.0%
Memory size	14.9 KiB

1	231

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	231
Distinct characters	1
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	1
2nd row	1
3rd row	1
4th row	1
5th row	1

Common Values

Value	Count	Frequency (%)
1	231	100.0%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
1	231	100.0%

Most occurring characters

Value	Count	Frequency (%)
1	231	100.0%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	231	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
1	231	100.0%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	231	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
1	231	100.0%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	231	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
1	231	100.0%

Interactions

Correlations

	Estado	TipoSocietario	anio_preinscripcion	antiguedad	cant_Apoderado	cant_MontoLimite	cant_antecedentes	cant_apercibimientos	cant_autenticado	cant_noAutenticado	cant_procesos_adjudicado	cant_representante	cant_sinMontoLimite	cant_socios	cant_suspensiones	dcant_procesos_adjudicado	dmonto_total_adjudicado	dtotal_articulos_provee	monto_total_adjudicado	periodo_preinscripcion	provincia	total_articulos_provee
Estado	1.000	0.335	0.091	0.127	0.000	1.000	0.000	0.000	0.167	0.000	0.000	0.000	0.000	0.000	0.000	0.141	0.383	0.071	0.000	0.164	0.370	0.000
TipoSocietario	0.335	1.000	0.182	0.214	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.096	0.230	0.000	0.062	0.290	0.227	0.426	0.190	0.353	0.000
anio_preinscripcion	0.091	0.182	1.000	-1.000	0.000	0.000	0.068	0.000	0.000	0.000	-0.205	0.000	0.000	0.000	0.071	0.174	0.182	0.000	-0.239	0.932	0.199	-0.030
antiguedad	0.127	0.214	-1.000	1.000	0.000	0.000	-0.068	0.000	0.000	0.000	0.205	0.000	0.000	0.000	-0.071	0.139	0.179	0.000	0.239	-0.932	0.239	0.030
cant_Apoderado	0.000	0.000	0.000	0.000	1.000	0.816	0.000	0.000	0.487	0.608	0.000	0.434	0.583	0.000	0.000	0.000	0.000	0.218	0.000	0.000	0.000	0.000
cant_MontoLimite	1.000	0.000	0.000	0.000	0.816	1.000	0.866	1.000	0.000	0.000	0.000	1.000	0.000	0.000	1.000	0.707	0.707	0.000	1.000	0.000	0.000	1.000
cant_antecedentes	0.000	0.000	0.068	-0.068	0.000	0.866	1.000	0.333	0.052	0.000	0.003	0.242	0.000	0.000	0.764	0.041	0.000	0.000	-0.131	0.082	0.272	0.192
cant_apercibimientos	0.000	0.000	0.000	0.000	0.000	1.000	0.333	1.000	0.000	1.000	0.000	0.000	0.000	0.000	0.000	0.282	0.183	0.000	0.000	0.000	0.000	0.116
cant_autenticado	0.167	0.000	0.000	0.000	0.487	0.000	0.052	0.000	1.000	0.000	0.000	0.515	0.550	0.001	0.000	0.000	0.000	0.000	0.000	0.153	0.193	0.000
cant_noAutenticado	0.000	0.000	0.000	0.000	0.608	0.000	0.000	1.000	0.000	1.000	0.000	0.000	0.555	0.149	0.000	0.000	0.293	0.635	0.000	0.000	0.000	0.000
cant_procesos_adjudicado	0.000	0.000	-0.205	0.205	0.000	0.000	0.003	0.000	0.000	0.000	1.000	0.000	0.000	0.072	-0.220	0.000	0.000	0.000	0.652	-0.221	0.000	0.378
cant_representante	0.000	0.000	0.000	0.000	0.434	1.000	0.242	0.000	0.515	0.000	0.000	1.000	0.408	0.164	0.400	0.252	0.430	0.000	0.093	0.000	0.000	0.148
cant_sinMontoLimite	0.000	0.096	0.000	0.000	0.583	0.000	0.000	0.000	0.550	0.555	0.000	0.408	1.000	0.000	0.000	0.000	0.000	0.132	0.101	0.070	0.000	0.000
cant_socios	0.000	0.230	0.000	0.000	0.000	0.000	0.000	0.000	0.001	0.149	0.072	0.164	0.000	1.000	0.000	0.102	0.000	0.075	0.203	0.000	0.194	0.000
cant_suspensiones	0.000	0.000	0.071	-0.071	0.000	1.000	0.764	0.000	0.000	0.000	-0.220	0.400	0.000	0.000	1.000	0.099	0.152	0.000	-0.295	0.083	0.332	0.162
dcant_procesos_adjudicado	0.141	0.062	0.174	0.139	0.000	0.707	0.041	0.282	0.000	0.000	0.000	0.252	0.000	0.102	0.099	1.000	0.315	0.151	0.000	0.150	0.102	0.000
dmonto_total_adjudicado	0.383	0.290	0.182	0.179	0.000	0.707	0.000	0.183	0.000	0.293	0.000	0.430	0.000	0.000	0.152	0.315	1.000	0.037	0.000	0.167	0.225	0.000
dtotal_articulos_provee	0.071	0.227	0.000	0.000	0.218	0.000	0.000	0.000	0.000	0.635	0.000	0.000	0.132	0.075	0.000	0.151	0.037	1.000	0.214	0.000	0.149	0.043
monto_total_adjudicado	0.000	0.426	-0.239	0.239	0.000	1.000	-0.131	0.000	0.000	0.000	0.652	0.093	0.101	0.203	-0.295	0.000	0.000	0.214	1.000	-0.270	0.000	0.115
periodo_preinscripcion	0.164	0.190	0.932	-0.932	0.000	0.000	0.082	0.000	0.153	0.000	-0.221	0.000	0.070	0.000	0.083	0.150	0.167	0.000	-0.270	1.000	0.306	-0.030
provincia	0.370	0.353	0.199	0.239	0.000	0.000	0.272	0.000	0.193	0.000	0.000	0.000	0.000	0.194	0.332	0.102	0.225	0.149	0.000	0.306	1.000	0.000
total_articulos_provee	0.000	0.000	-0.030	0.030	0.000	1.000	0.192	0.116	0.000	0.000	0.378	0.148	0.000	0.000	0.162	0.000	0.000	0.043	0.115	-0.030	0.000	1.000

Missing values

A simple visualization of nullity by column.

Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

	CUIT	Nombre	FechaPreinscripcion	Estado	TipoSocietario	periodo_preinscripcion	anio_preinscripcion	cant_procesos_adjudicado	monto_total_adjudicado	antiguedad	provincia	cant_socios	cant_apercibimientos	cant_suspensiones	cant_antecedentes	cant_Apoderado	cant_representante	cant_autenticado	cant_noAutenticado	cant_sinMontoLimite	cant_MontoLimite	total_articulos_provee	dmonto_total_adjudicado	dcant_procesos_adjudicado	dtotal_articulos_provee	Cluster_6
2	30711500363	LICICOM S.R.L.	15/09/2016	Inscripto	S.R.L	201609	2016	22.0	7.425067e+06	5.0	CABA	2.0	1.0	2.0	4.0	2.0	1.0	2.0	1.0	3.0	NaN	105.0	(6.702.697- 9.424.898]	(19.0, 39.0]	(97.6, 161.0]	1
23	30590151013	VIDITEC S.A..	22/07/2016	Inscripto	Sociedad Anónima	201607	2016	47.0	2.714623e+07	5.0	CABA	5.0	1.0	NaN	1.0	1.0	NaN	1.0	NaN	1.0	NaN	113.0	(19.975.532- 30.451.916]	(39.0, 1214.0]	(97.6, 161.0]	1
24	30678561165	NACION SEGUROS S.A.	15/11/2016	Inscripto	Sociedad Anónima	201611	2016	1102.0	6.933819e+09	5.0	CABA	5.0	NaN	NaN	NaN	1.0	NaN	1.0	NaN	1.0	NaN	26.0	(222.964.579- 46.172.150.151]	(39.0, 1214.0]	(21.0, 29.0]	1
29	30591267759	ERRE-DE SRL	02/08/2016	Inscripto	S.R.L	201608	2016	340.0	1.379889e+08	5.0	Buenos Aires	2.0	1.0	NaN	1.0	3.0	NaN	1.0	2.0	2.0	1.0	226.0	(89.439.449- 222.964.579]	(39.0, 1214.0]	(161.0, 345.0]	1
35	30702024834	Datastar Argentina S.A.	09/09/2016	Inscripto	Sociedad Anónima	201609	2016	111.0	3.182254e+09	5.0	CABA	2.0	1.0	NaN	1.0	2.0	2.0	2.0	2.0	4.0	NaN	207.0	(222.964.579- 46.172.150.151]	(39.0, 1214.0]	(161.0, 345.0]	1
39	30623295946	LAVIERI HNOS DE LAVIERI SEBASTIAN GABRIEL LAVIERI ALEJANDRO CARLOS.	29/09/2016	Inscripto	Sociedades De Hecho	201609	2016	22.0	7.831100e+06	5.0	CABA	2.0	1.0	NaN	1.0	NaN	2.0	2.0	NaN	2.0	NaN	37.0	(6.702.697- 9.424.898]	(19.0, 39.0]	(29.0, 40.0]	1
60	30673249902	LOMAS DEL SOL SRL	21/10/2016	Suspendido	S.R.L	201610	2016	27.0	1.375720e+09	5.0	San Juan	2.0	NaN	4.0	4.0	1.0	NaN	1.0	NaN	1.0	NaN	602.0	(222.964.579- 46.172.150.151]	(19.0, 39.0]	(345.0, 6993.0]	1
85	30714236888	NANOTEC S.R.L.	30/09/2016	Inscripto	S.R.L	201609	2016	12.0	7.242921e+06	5.0	CABA	2.0	1.0	NaN	1.0	1.0	NaN	1.0	NaN	1.0	NaN	160.0	(6.702.697- 9.424.898]	(8.0, 12.0]	(97.6, 161.0]	1
89	30710362218	Licenciasonline SA	13/09/2016	Inscripto	Sociedad Anónima	201609	2016	1.0	8.607014e+07	5.0	CABA	4.0	1.0	NaN	1.0	1.0	1.0	1.0	1.0	2.0	NaN	3.0	(46.718.747- 89.439.449]	(0.999, 2.0]	(2.0, 3.0]	1
103	30710828608	INFORMÁTICA PALMAR SRL	04/10/2016	Inscripto	S.R.L	201610	2016	143.0	6.447596e+07	5.0	CABA	2.0	1.0	NaN	1.0	NaN	2.0	1.0	1.0	2.0	NaN	37.0	(46.718.747- 89.439.449]	(39.0, 1214.0]	(29.0, 40.0]	1

	CUIT	Nombre	FechaPreinscripcion	Estado	TipoSocietario	periodo_preinscripcion	anio_preinscripcion	cant_procesos_adjudicado	monto_total_adjudicado	antiguedad	provincia	cant_socios	cant_apercibimientos	cant_suspensiones	cant_antecedentes	cant_Apoderado	cant_representante	cant_autenticado	cant_noAutenticado	cant_sinMontoLimite	cant_MontoLimite	total_articulos_provee	dmonto_total_adjudicado	dcant_procesos_adjudicado	dtotal_articulos_provee	Cluster_6
8150	30715995979	DESARROLLOS URBANOS RIO DE LA PLATA SRL	16/09/2020	Inscripto	S.R.L	202009	2020	18.0	2.095676e+08	1.0	CABA	2.0	1.0	NaN	1.0	NaN	1.0	1.0	NaN	1.0	NaN	18.0	(89.439.449- 222.964.579]	(12.0, 19.0]	(15.0, 21.0]	1
8165	30709585831	SECON SECURITY CONCEPT SA	11/04/2017	Inscripto	Sociedad Anónima	201704	2017	1.0	1.326591e+07	4.0	Buenos Aires	2.0	1.0	NaN	1.0	1.0	1.0	1.0	1.0	1.0	1.0	102.0	(9.424.898- 13.557.176]	(0.999, 2.0]	(97.6, 161.0]	1
8193	20402312514	Lionel Druscovich	12/11/2021	Inscripto	Persona Física	202111	2021	4.0	1.198617e+06	0.0	CABA	NaN	NaN	NaN	NaN	1.0	NaN	1.0	NaN	1.0	NaN	4471.0	(890.758- 1.302.657]	(3.0, 4.0]	(345.0, 6993.0]	1
8668	30701547531	ASTILLEROS PATAGONICOS INTEGRADOS	16/08/2018	Inscripto	Sociedad Anónima	201808	2018	2.0	5.357829e+07	3.0	Buenos Aires	1.0	NaN	1.0	1.0	1.0	1.0	1.0	1.0	2.0	NaN	17.0	(46.718.747- 89.439.449]	(0.999, 2.0]	(15.0, 21.0]	1
8956	27181287077	FABIANA SANDRA CORTES	10/04/2017	Inscripto	Persona Física	201704	2017	13.0	1.682270e+07	4.0	Buenos Aires	NaN	1.0	7.0	8.0	1.0	NaN	1.0	NaN	1.0	NaN	254.0	(13.557.176- 19.975.532]	(12.0, 19.0]	(161.0, 345.0]	1
8957	A28006104	AIRBUS DEFENCE AND SPACE S.A.	21/06/2019	Pre Inscripto	PJ Extranjero Sin Sucursal	201906	2019	3.0	8.494348e+04	2.0	Extranjera	1.0	1.0	NaN	1.0	2.0	NaN	2.0	NaN	2.0	NaN	14.0	(33.011- 104.767]	(2.0, 3.0]	(11.0, 15.0]	1
9237	214349010016	FARINTO S.A.	05/07/2019	Pre Inscripto	PJ Extranjero Sin Sucursal	201907	2019	NaN	NaN	2.0	Extranjera	1.0	1.0	NaN	1.0	1.0	NaN	1.0	NaN	1.0	NaN	6.0	nan	NaN	(4.0, 6.0]	1
9504	30716582082	BATERIAS ECOBAT S.A.S	16/10/2020	Inscripto	Otras Formas Societarias	202010	2020	1.0	8.288520e+05	1.0	San Juan	1.0	1.0	NaN	1.0	NaN	1.0	1.0	NaN	1.0	NaN	1.0	(599.760- 890.758]	(0.999, 2.0]	(0.999, 2.0]	1
9716	20365997005	Alejandro Javier thea	20/02/2022	Inscripto	Persona Física	202202	2022	19.0	6.293224e+06	0.0	Buenos Aires	NaN	NaN	NaN	NaN	1.0	NaN	1.0	NaN	1.0	NaN	5612.0	(4.727.330- 6.702.697]	(12.0, 19.0]	(345.0, 6993.0]	1
9783	30707835563	SUTEL S.R.L.	25/08/2016	Inscripto	S.R.L	201608	2016	2.0	3.889114e+06	5.0	Buenos Aires	2.0	NaN	2.0	2.0	NaN	1.0	1.0	NaN	1.0	NaN	329.0	(3.396.600- 4.727.330]	(0.999, 2.0]	(161.0, 345.0]	1

Overview

Variables

Most occurring characters

Most occurring categories

Most frequent character per category

(unknown)

Most occurring scripts

Most frequent character per script

(unknown)

Most occurring blocks

Most frequent character per block

(unknown)

Most occurring characters

Most occurring categories

Most frequent character per category

(unknown)

Most occurring scripts

Most frequent character per script

(unknown)

Most occurring blocks

Most frequent character per block

(unknown)

Common Values

Length

Common Values (Plot)

Most occurring characters

Most occurring categories

Most frequent character per category

(unknown)

Most occurring scripts

Most frequent character per script

(unknown)

Most occurring blocks

Most frequent character per block

(unknown)

Common Values

Length

Common Values (Plot)

Most occurring characters

Most occurring categories

Most frequent character per category

(unknown)

Most occurring scripts

Most frequent character per script

(unknown)

Most occurring blocks

Most frequent character per block

(unknown)

Common Values

Length

Most occurring characters

Most occurring categories

Most frequent character per category

(unknown)

Most occurring scripts

Most frequent character per script

(unknown)

Most occurring blocks

Most frequent character per block

(unknown)

Common Values

Length

Common Values (Plot)

Most occurring characters

Most occurring categories

Most frequent character per category

(unknown)

Most occurring scripts

Most frequent character per script

(unknown)

Most occurring blocks

Most frequent character per block

(unknown)

Common Values

Length

Common Values (Plot)

Most occurring characters

Most occurring categories

Most frequent character per category

(unknown)